(1) Field of Invention
The present invention relates to a system for object tracking and, more particularly, to a system for object tracking using motion-based object detection and an enhanced Kalman-type filtering.
(2) Description of Related Art
Object tracking remains an unsolved problem in the computer vision and machine learning society. Many advanced approaches often rely on hand-crafted models and require complex computation, making them rather “expensive” for resource-restrictive and large-throughput-required applications. Motion-based object detection (MogS) technology is described in U.S. application Ser. No. 13/669,269 (hereinafter referred to as the '269 application) and U.S. application Ser. No. 13/743,742 (hereinafter referred to as the '742 application), both of which are hereby incorporated by reference as though fully set forth herein. MogS is based on a simple, yet powerful, background modeling and learning approach. Given an online updated background model, moving objects can be detected simply by measuring the difference between the current frame and the background model. Nevertheless, MogS itself does not have tracking capability and, therefore, may miss detecting the objects in different places.
The Kalman filter is a process that uses a series of measurements observed over time, containing noise and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone, as described by D. Simon in “Kalman Filtering with State Constraints: A Survey of Linear and Nonlinear Algorithms,” IET Control Theory & Applications, Volume 4, Issue 8, August 2010, pp. 1303-1318, which is hereby incorporated by reference as though fully set forth herein. The Kalman filter is a widely used simple and efficient tracking technique. The conventional Kalman filter can be added into any object detection process to form a simple object tracking system. Using the target location detected by MogS, one could apply Kalman filtering to predict and track the object's moving trajectory. However, the conventional Kalman filter is too simple to deal with the loss of tracking issue for MogS detection.
Each of the prior methods described above exhibit limitations that prevent them from being able to deal with the loss of tracking issue. Thus, a continuing need exists for an enhanced Kalman filtering process that improves motion tracking.